Bayesian analysis and model selection of garch models with additive jumps

Christian Haefke, Leopold Soegner

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This article investigates parameter estimation and model selection of GARCH models with additive jumps. Continuous noise is driven by Student-t innovations. Since the likelihood is not available in closed form, Bayesian simulation methods are applied to estimate the model parameters and perform model selection. Simulations suggest that the parameters of the jump process are difficult to estimate. Informative priors based on sample moments and tests on jumps are necessary to obtain reliable parameter estimates. In an application using S&P 500returns we estimate a 3% jump intensity. In addition, our model allows us to infer the impact of a jump on future volatility. Our estimates show that the impact of jumps on the conditional volatility is large compared to the impact of continuous innovations.

Original languageEnglish (US)
Title of host publicationRecent Advances and Future Directions in Causality, Prediction, and Specification Analysis
Subtitle of host publicationEssays in Honor of Halbert L. White Jr
PublisherSpringer New York
Pages179-208
Number of pages30
ISBN (Electronic)9781461416531
ISBN (Print)9781461416524
DOIs
StatePublished - Jan 1 2013

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Keywords

  • Additive Jumps
  • Bayes Factors
  • Garch
  • Model Selection

ASJC Scopus subject areas

  • Economics, Econometrics and Finance(all)
  • Business, Management and Accounting(all)

Cite this

Haefke, C., & Soegner, L. (2013). Bayesian analysis and model selection of garch models with additive jumps. In Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis: Essays in Honor of Halbert L. White Jr (pp. 179-208). Springer New York. https://doi.org/10.1007/978-1-4614-1653-1_7